The approach

Our goal has always been to make the process of data analysis and modeling more effective. In more than 10 years of research and clinical development we have established, together with our partners and in collaboration with clinics, a new approach and the related Artificial Intelligence based software environment. Based on clinical data new methods and tools have been derived, exceeding the capabilities of so far available software.

The Exploris AI-X-Platform allows for fast development of novel diagnostic and therapeutic solutions, completely independent of disease areas. We already have prominent examples unveiling the potential of our AI-X-Platform. Beside the Cardioexplorer test which detects stenosis in coronary arteries better than for example the stress ECG, we are developing Heart Failure Explorer and Breast Cancer Explorer which allow for a personalized therapy, resulting in better outcome and more safety and convenience for patients.

Based on the hidden information in the data, the software selects automatically the best combination of methods and allocates the respective software components. Running the parallelized fully automated exploration and optimization process, the software is able to

-detect complex non-linear patterns even in data of very high dimensionality

-extract relevant biomarkers and their combinations

-measure and attribute the risks to specific ranges of values and thresholds.

Starting point is always just data of the concerned realm. This approach allows for acquisition of new knowledge about the disease, often resulting in detection of new markers. Furthermore the productivity of modelers is increased. While in the classical approach modelers often end up with one model, our software suite generates dozens of models which get validated by the software suite itself. This approach avoids over fitting and leads to more robust models.

Improving the quality of modeling is a primary precondition for model based diagnostics and therapeutics.

The proprietary platform detects hidden relationship in any data independent of the realm and provides model-based tests for different diagnostic and therapeutic areas.